Targeting the MR1-MAIT cell axis improves vaccine efficacy and affords protection against viral pathogens
Bibliographic record
Abstract
Mucosa-associated invariant T (MAIT) cells are MR1-restricted, innate-like T lymphocytes with tremendous antibacterial and immunomodulatory functions. Additionally, MAIT cells sense and respond to viral infections in an MR1-independent fashion. However, whether they can be directly targeted in immunization strategies against viral pathogens is unclear. We addressed this question in multiple wild-type and genetically altered but clinically relevant mouse strains using several vaccine platforms against influenza viruses, poxviruses and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We demonstrate that 5-(2-oxopropylideneamino)-6-D-ribitylaminouracil (5-OP-RU), a riboflavin-based MR1 ligand of bacterial origin, can synergize with viral vaccines to expand MAIT cells in multiple tissues, reprogram them towards a pro-inflammatory MAIT1 phenotype, license them to bolster virus-specific CD8+ T cell responses, and potentiate heterosubtypic anti-influenza protection. Repeated 5-OP-RU administration did not render MAIT cells anergic, thus allowing for its inclusion in prime-boost immunization protocols. Mechanistically, tissue MAIT cell accumulation was due to their robust proliferation, as opposed to altered migratory behavior, and required viral vaccine replication competency and Toll-like receptor 3 and type I interferon receptor signaling. The observed phenomenon was reproducible in female and male mice, and in both young and old animals. It could also be recapitulated in a human cell culture system in which peripheral blood mononuclear cells were exposed to replicating virions and 5-OP-RU. In conclusion, although viruses and virus-based vaccines are devoid of the riboflavin biosynthesis machinery that supplies MR1 ligands, targeting MR1 enhances the efficacy of vaccine-elicited antiviral immunity. We propose 5-OP-RU as a non-classic but potent and versatile vaccine adjuvant against respiratory viruses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".